The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
GPU Implementation of Friend Recommendation System using CUDA for Social Networking Services
|
|
Author(s): K. G. Srinivasa (M. S. Ramaiah Institute of Technology, India), G. M. Siddesh (M. S. Ramaiah Institute of Technology, India), Srinidhi Hiriyannaiah (M. S. Ramaiah Institute of Technology, India), Kushagra Mishra (M. S. Ramaiah Institute of Technology, India), Coca Sai Prajeeth (M. S. Ramaiah Institute of Technology, India)and Ameen Mohammed Talha (M. S. Ramaiah Institute of Technology, India)
Copyright: 2016
Pages: 16
Source title:
Emerging Research Surrounding Power Consumption and Performance Issues in Utility Computing
Source Author(s)/Editor(s): Ganesh Chandra Deka (Regional Vocational Training Institute (RVTI) for Women, India), G.M. Siddesh (Ramaiah Institute of Technology, India), K. G. Srinivasa (M S Ramaiah Institute of Technology, Bangalore, India)and L.M. Patnaik (IISc, Bangalore, India)
DOI: 10.4018/978-1-4666-8853-7.ch015
Purchase
|
Abstract
Nowadays hybrid recommender systems are used, which utilize both collaborative and content based filtering techniques unlike the FoF system that have been presented in the chapter. Social networking services (SNSs) provide a platform where likeminded people interact and express opinions. The trends of socializing have changed drastically and the general population is turning to these services to socialize and network with new people. Massive infrastructure compliments uninterrupted usage of these services. Owing to the rapidly growing user base of SNSs, there is always a need to improve upon the existing infrastructure to keep cost and performance in tune with one another. GPUs are proving to be a viable solution to bridge the gap between the two. In this chapter, we describe GPU implementation of a Friend recommender system which is based on content-based filtering mechanism. It has given significant speed up from its previous counterparts, thus making the whole process more efficient.
Related Content
|
Radhika Kavuri, Satya kiranmai Tadepalli.
© 2024.
19 pages.
|
|
Ramu Kuchipudi, Ramesh Babu Palamakula, T. Satyanarayana Murthy.
© 2024.
10 pages.
|
|
Nidhi Niraj Worah, Megharani Patil.
© 2024.
21 pages.
|
|
Vishal Goar, Nagendra Singh Yadav.
© 2024.
23 pages.
|
|
S. Boopathi.
© 2024.
24 pages.
|
|
Sai Samin Varma Pusapati.
© 2024.
25 pages.
|
|
Swapna Mudrakola, Krishna Keerthi Chennam, Shitharth Selvarajan.
© 2024.
11 pages.
|
|
|